Mining traffic data for road incidents detection

Gakis, Evangelos; Kehagias, Dionysios; Tzovaras, Dimitrios

doi:10.1109/itsc.2014.6957808

Cited by 14 publications

(6 citation statements)

References 12 publications

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“…On the other hand, non-recurrent congestion in a road network is mainly caused by incidents, workzones, special events and extreme weather [45]. Comparatively, the identification of non-recurrent congestion is much difficult, which is often dealt as a pattern recognition problem and many classifiers are utilized to determine the locations and severities of non-recurrent congestion [46], [47]. In this paper, to identify the long-term traffic bottlenecks in urban traffic networks, we mainly concern about the identification of recurrent congestion.…”

Section: A Congestion and Congestion Correlationmentioning

confidence: 99%

Congestion Propagation Based Bottleneck Identification in Urban Road Networks

Yue

Mao

et al. 2020

IEEE Trans. Veh. Technol.

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Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this article, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method.

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Section: A Congestion and Congestion Correlationmentioning

confidence: 99%

Congestion Propagation Based Bottleneck Identification in Urban Road Networks

Yue

Mao

et al. 2020

IEEE Trans. Veh. Technol.

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“…In this section, we validate the traffic incident detection algorithm using the following measures [66]:…”

Section: Validation Of the Algorithm For Traffic Incidents Detectionmentioning

confidence: 99%

WATI: Warning of Traffic Incidents for Fuel Saving

Magaña

Muñoz-Organero

2016

Mobile Information Systems

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Traffic incidents (heavy traffic, adverse weather conditions, and traffic accidents) cause an increase in the frequency and intensity of the acceleration and deceleration. The result is a very significant increase in fuel consumption. In this paper, we propose a solution to reduce the impact of such events on energy consumption. The solution detects the traffic incidents based on measured telemetry data from vehicles and the different driver profiles. The proposal takes into account the rolling resistance coefficient, the road slope angle, and the vehicles speeds, from vehicles which are on the scene of the traffic incident, in order to estimate the optimal deceleration profile. Adapted advice and feedback are provided to the drivers in order to appropriately and timely release the accelerator pedal. The expert system is implemented on Android mobile devices and has been validated using a dataset of 150 tests using 15 different drivers. The main contribution of this paper is the proposal of a system to detect traffic incidents and provide an optimal deceleration pattern for the driver to follow without requiring sensors on the road. The results show an improvement on the fuel consumption of up to 13.47%.

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“…The study of spatial-temporal transit volume distribution in urban environments is a central aspect for guiding decision-making processes according to the current traffic situation, and can be approached in several different modalities and in a wide variety of contexts [1,2]. Typical applications include traffic congestion warnings [3,4], car accident risk assessments [5,6], and pollution measurement estimations [7,8].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Spatially-Distributed Urban Traffic Volumes via Multi-Target LSTM-Based Neural Network Regressor

Crivellari

Beinat

2020

Mathematics

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Monitoring the distribution of vehicles across the city is of great importance for urban traffic control. In particular, information on the number of vehicles entering and leaving a city, or moving between urban areas, gives a valuable estimate on potential bottlenecks and congestions. The possibility of predicting such flows in advance is even more beneficial, allowing for timely traffic management strategies and targeted congestion warnings. Our work is inserted in the context of short-term forecasting, aiming to predict rapid changes and sudden variations in the traffic volume, beyond the general trend. Moreover, it concurrently targets multiple locations in the city, providing an instant prediction outcome comprising the future distribution of vehicles across several urban locations. Specifically, we propose a multi-target deep learning regressor for simultaneous predictions of traffic volumes, in multiple entry and exit points among city neighborhoods. The experiment focuses on an hourly forecasting of the amount of vehicles accessing and moving between New York City neighborhoods through the Metropolitan Transportation Authority (MTA) bridges and tunnels. By leveraging a single training process for all location points, and an instant one-step volume inference for every location at each time update, our sequential modeling approach is able to grasp rapid variations in the time series and process the collective information of all entry and exit points, whose distinct predicted values are outputted at once. The multi-target model, based on long short-term memory (LSTM) recurrent neural network layers, was tested on a real-world dataset, achieving an average prediction error of 7% and demonstrating its feasibility for short-term spatially-distributed urban traffic forecasting.

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Mining traffic data for road incidents detection

Cited by 14 publications

References 12 publications

Congestion Propagation Based Bottleneck Identification in Urban Road Networks

Congestion Propagation Based Bottleneck Identification in Urban Road Networks

WATI: Warning of Traffic Incidents for Fuel Saving

Forecasting Spatially-Distributed Urban Traffic Volumes via Multi-Target LSTM-Based Neural Network Regressor

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